scholarly journals Quantum enhanced cross-validation for near-optimal neural networks architecture selection

2018 ◽  
Vol 16 (08) ◽  
pp. 1840005 ◽  
Author(s):  
Priscila G. M. dos Santos ◽  
Rodrigo S. Sousa ◽  
Ismael C. S. Araujo ◽  
Adenilton J. da Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Analysis ◽  
Cross Validation ◽  
Quantum Memory ◽  
Classical Algorithm ◽  
Artificial Neural ◽  
Quantum Speedup

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networks (ANN) in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

scholarly journals Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

2019 ◽  
Vol 9 (17) ◽  
pp. 3502 ◽  
Author(s):  
Nicola Baldo ◽  
Evangelos Manthos ◽  
Matteo Miani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cross Validation ◽  
Laboratory Data ◽  
Stiffness Modulus ◽  
Artificial Neural ◽  
Marshall Stability ◽  
Mean Square Errors ◽  
Fold Cross Validation

The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

scholarly journals Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1590
Author(s):  
Ekaterini Hadjisolomou ◽  
Konstantinos Stefanidis ◽  
Herodotos Herodotou ◽  
Michalis Michaelides ◽  
George Papatheodorou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Chlorophyll A ◽  
Cross Validation ◽  
Computational Time ◽  
Data Set ◽  
K Value ◽  
Artificial Neural ◽  
Fold Cross Validation

Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.

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